Dat Duong

A learner. In crypto now.

[Julie Zhuo @joulee]

The only way I know of to develop better intuition for your own product is to:

1. Constantly use your product as a real customer would
2. Constantly research your target customer

12 ways to make the above practical in your week-to-week:

1. Use the product daily as a real user would [15 minutes / day]
2. Watch one or two user research or replay sessions [10 minutes / day]
3. Check your key usage metrics dashboard [5 minutes / day]
4. Interview a prospective client and ask them to describe a specific workflow related to your product [30 min / week]
5. Email or slack 1-3 existing clients with a specific feedback question [30 min / week]
6. Read notes from recent sales calls or user interviews [30 minutes / week]
7. Read customer feedback queue tickets [30 minutes / week]
8. Read the latest data analysis on user behavior on your product [30 minutes / week]
9. Read industry blogs / articles related to your product area, ideally about either customer learnings or from the perspective of customers [30 minutes / week]
10. Explore competitor products as a real user would [2 hours / month]
11. Try selling the product yourself, or tag along on a sales call [2 hours / month]
12. Read 3 books a year relevant to the psychology of your customers

If you did EVERYTHING on the list above, it would take you 10-15% of your working hours. Doing half of them would be more like 5% of your week.

This is a tiny investment for
1) honing your product intuition
2) making better prioritization decisions
3) gaining greater conviction in your work

and frankly 4) having way more fun in your work.

[Bill Ackman @BillAckman]

.@BernieSanders , it is a time to celebrate. @elonmusk has created enormous value for society by building @SpaceX, driving down the cost of rocket launches and creating a global satellite communication network that has brought high speed, low-cost internet and communication access to hundreds of millions and eventually billions of people along with critical advantages for our military and our nation’s defense.

SpaceX and its technologies will cause an acceleration in the growth of wages and wealth creation globally, including in some of the poorest communities in the U.S. and around the world.

Access to low-cost, high speed communications everywhere will allow children around the world to be educated, families to build businesses, and life-saving medical knowledge and care to be available everywhere.

SpaceX will materially bring down the cost of compute, advancing AI and humanity.

Meanwhile, 4,000 SpaceX employees yesterday became millionaires, including hourly wage employees who you claim you are trying to help.

The Elon Musks of the world drive growth, global GDP, and provide access to goods and services at lower cost that would otherwise not exist.

Elon’s nominal trillionaire status is due to his ownership of SpaceX, Tesla, Neuralink, the Boring Company and his other initiatives that have brought new technologies that improve our everyday lives.

Elon is not sitting on a trillion dollar pile of cash, jewelry and gold. He is using his controlling stakes in his companies to advance mankind. Elon’s companies don’t pay dividends. They reinvest all of their capital to accelerate innovation and value creation.

Elon is working 24/7 for all of us. He deserves respect and appreciation, not smears.

Bernie, your socialism would never allow a SpaceX to be built. Socialism has only proven to impoverish mankind and lead to death and destruction.

We need to create the conditions for more SpaceXs to be built, not attack the great entrepreneurs who are helping to advance our country.

[Reads with Ravi @readswithravi]

Take the risk.

[Reads with Ravi @readswithravi]

The person you will be in 5 years depends largely on:

1. The books you read
2. The food you eat
3. The habits you cultivate
4. The people you surround yourself with
5. The conversations you engage in
6. The mindset you adopt
7. The risks you take and lessons you learn

[Ronit Pereira @CAronitpereira]

In 2009, Berkshire Hathaway share was down 50% from it’s peak.

Charlie Munger was asked about how he felt about this. Listen to his Cold Blooded answer 🥶

Reporter: “How worried are you by the 50% decline in the Berkshire Hathaway share?”

Charlie: “Zero. This is the 3rd time that Warren and I have seen our stock holdings fall by 50% or more.”

“In fact, you should react with equanimity to market price decline of 50% or more in your portfolio at-least 2-3 times a century.”

“If you don’t, you’re not fit to be a common shareholder and you deserve the mediocre results you’re going to get. 🔥

[Reads with Ravi @readswithravi]

“Most older, successful people will tell you that their life was best when they lived it unapologetically, on their own terms.”

— Naval Ravikant

[Aaron Levie @levie]

Whether it’s existing consulting firms, new ones that emerge, FDEs from agent vendors, or new internal agent engineering roles, the amount of work that is going to be created to implement agents in enterprises will exceed anything we imagine today.

The complexity of implementing agents in any existing organizations is very real. When I talk to large enterprises, as you move from a chat paradigm to agents that participate in meaningful workflows, there are a number of things they need to do.

First, you have to get agents to be able to talk to your data securely across your systems. In many cases, enterprises have decades of legacy infrastructure that contain the valuable context for AI agents. That’s going to take a ton of work to go modernize and move to systems that work well with agents.

Then, you need to ensure that you’ve implemented agents with the right access controls and entitlements, the right scopes to be safely used, and have ways of monitoring, logging, and securing the work that they do.

Next, you need to actually document the processes in the organization in a way that agents can utilize for doing the work. You also need to figure out what the new workflow looks like when agents and people are working together on a process, and who steps in where. Just replicating the old workflow will mute the gains. Oh and you likely need to create evals for your top new end-state processes.

Finally, you have to keep up with a rapidly changing set of best practices and architectural shifts happening in the agent space. While it’s fun for people to change their personal productivity tools on a dime, it’s 100X harder to do this in a business process. The speed of change is a blessing and a curse right now for anyone trying to keep a stable system design.

All of this means that individuals and companies that develop expertise on the above set of components (and more) are going to be needed to help organizations actually implement agents at scale. This is also the rationale for vertical AI agents right now that can go in deep on a business domain and help bring automation to it.

This is a huge opportunity right now whether you’re doing this internally or as an external business provider.

[Haseeb ＞|＜ @hosseeb]

The highest-value human work in the AI era will be in domains with sparse reward signals. Internalize this, or watch your value erode over the next decade.

Math, programming, rote memorization, data science, all fucked. The classic “smart nerd” jobs are exactly where AI is strongest, because the feedback loops are dense. You can check the answer. You can run the test. That means AI can improve quickly, and humans will rapidly fall behind.

Your advantage as a human is in messy domains.

Taste. Judgment. Negotiation. Risk-taking. Politics. Sales. Science at the frontier. Anything you can only really learn by doing. Cross-disciplinary stuff.

The valuable domains will be the ones guarded by secrets, tacit knowledge, weak labels, long feedback cycles, and ambiguous outcomes. Places where the training data is scarce, the ground truth is disputed, and it's impossible to explain why something is good.

AI will still enter these domains. But we will be slower to trust it unsupervised there, because it will be harder to tell when it is right, harder to prove when it is wrong, and difficult to construct secure sandboxes. The stakes will be too high to YOLO it.

I find myself saying this over and over again to young people today: the future does not belong to people who are able to get good grades on tests. It belongs to people who can operate under uncertainty, in domains where correctness is hard to define.

Those domains will become the thin waist of the economy: as productivity everywhere else accelerates, the humans who excel there will become our economic Strait of Hormuz. The best humans in these domains will demand an enormous cut of the growing economic pie.

Your imperative going forward is to make sure you're one of these people.

(Or become an electrician. That probably works too.)

[Naval @naval]

Truth, love, and beauty are all pursued for their own sake, even if they make us worse off.

[Antonio Linares @alc2022]

https://t.co/auOjU0ELSh

[Chamath Palihapitiya @chamath]

When your working life rewards you, it’s easy to ratchet up the complexity: homes, cars, travel, possessions etc.

I have found that all that complexity comes at the sake of your most fleeting asset: your time. Instead of building things, all of a sudden you’re dealing with minutiae and logistics. Instead of talking mostly to engineers, you’re talking mostly to non-engineers. The building stops…the business of managing self inflicted complexity begins.

It’s worth noting that the best players in the game (Buffett, Elon) have kept their life extremely basic, almost monastic/nomadic, as success ratcheted them ever higher.

I think it’s the biggest secret hiding in plain sight:

When the world upgrades your status, downgrade your complexity.

[Ryo Lu @ryolu_]

keep struggling

when things come too easy, you don’t exercise the brain nor the emotions. ease can feel like progress, but it often skips the reps that actually change you.

growth is usually a loop, not a straight line – you take passes. you try, you fail, you reframe. you come back with a slightly better model, a slightly calmer nervous system, a slightly wider range of what you can handle.

hardship isn’t the goal. but friction is gold. it shows you where your understanding is thin, where your habits are brittle, where your ego is doing the steering. the struggle is the curriculum.

agents are making things easier, and that’s good. but don’t confuse speed with depth. use AI to remove busywork, then spend the saved energy on the parts that still hurt a little: the unclear problem, the uncomfortable conversation, the hard tradeoffs, the things you can’t yet explain in words. instead of putting all your wishes into the black box, actually keep thinking, and seeing things fully.

keep the difficulty where it matters. outsource the tedious, keep the meaningful resistance. that’s how we keep learning – and how we stay human while your tools get superhuman.

[Matt Shumer @mattshumer_]

https://t.co/ivXRKXJvQg

[Machina @EXM7777]

https://t.co/j3AA90tjxQ

[Greg Brockman @gdb]

Software development is undergoing a renaissance in front of our eyes.

If you haven't used the tools recently, you likely are underestimating what you're missing. Since December, there's been a step function improvement in what tools like Codex can do. Some great engineers at OpenAI yesterday told me that their job has fundamentally changed since December. Prior to then, they could use Codex for unit tests; now it writes essentially all the code and does a great deal of their operations and debugging. Not everyone has yet made that leap, but it's usually because of factors besides the capability of the model.

Every company faces the same opportunity now, and navigating it well — just like with cloud computing or the Internet — requires careful thought. This post shares how OpenAI is currently approaching retooling our teams towards agentic software development. We're still learning and iterating, but here's how we're thinking about it right now:

As a first step, by March 31st, we're aiming that:

(1) For any technical task, the tool of first resort for humans is interacting with an agent rather than using an editor or terminal.
(2) The default way humans utilize agents is explicitly evaluated as safe, but also productive enough that most workflows do not need additional permissions.

In order to get there, here's what we recommended to the team a few weeks ago:

1. Take the time to try out the tools. The tools do sell themselves — many people have had amazing experiences with 5.2 in Codex, after having churned from codex web a few months ago. But many people are also so busy they haven't had a chance to try Codex yet or got stuck thinking "is there any way it could do X" rather than just trying.
- Designate an "agents captain" for your team — the primary person responsible for thinking about how agents can be brought into the teams' workflow.
- Share experiences or questions in a few designated internal channels
- Take a day for a company-wide Codex hackathon

2. Create skills and AGENTS[.md].
- Create and maintain an AGENTS[.md] for any project you work on; update the AGENTS[.md] whenever the agent does something wrong or struggles with a task.
- Write skills for anything that you get Codex to do, and commit it to the skills directory in a shared repository

3. Inventory and make accessible any internal tools.
- Maintain a list of tools that your team relies on, and make sure someone takes point on making it agent-accessible (such as via a CLI or MCP server).

4. Structure codebases to be agent-first. With the models changing so fast, this is still somewhat untrodden ground, and will require some exploration.
- Write tests which are quick to run, and create high-quality interfaces between components.

5. Say no to slop. Managing AI generated code at scale is an emerging problem, and will require new processes and conventions to keep code quality high
- Ensure that some human is accountable for any code that gets merged. As a code reviewer, maintain at least the same bar as you would for human-written code, and make sure the author understands what they're submitting.

6. Work on basic infra. There's a lot of room for everyone to build basic infrastructure, which can be guided by internal user feedback. The core tools are getting a lot better and more usable, but there's a lot of infrastructure that currently go around the tools, such as observability, tracking not just the committed code but the agent trajectories that led to them, and central management of the tools that agents are able to use.

Overall, adopting tools like Codex is not just a technical but also a deep cultural change, with a lot of downstream implications to figure out. We encourage every manager to drive this with their team, and to think through other action items — for example, per item 5 above, what else can prevent a lot of "functionally-correct but poorly-maintainable code" from creeping into codebases.

[Haseeb ＞|＜ @hosseeb]

On the one hand, AI influencers are breathlessly raving about Claude Code, Clawdbot, and Cowork. And on the other hand, most people I know—even software engineers—are despondent, overwhelmed about how everything is changing so quickly. I hear this from people early in their careers especially, a fear that everything they've learned and the skills they've gained are rapidly being devalued.

This is a mental trap. Don't fall for it. You should not just be watching from the sidelines or reading articles about "how software engineering is changing."

Imagine it was 1993 and the personal computer revolution was kicking off. If you could go back in time to then, what should you have done?

The answer: try everything. Buy a PC. Learn how to touch type. Figure out what the Internet is. Imbibe it all. Don't wait until it becomes a job requirement.

That's exactly what you should do with AI. Try everything. Try Claude Code, try Clawdbot, try the Excel integrations, Veo, everything you can get your hands on. Learn what it's doing. Build your intuitions. Be one step ahead of it. Evolve alongside it. Don't lose your curiosity or get swallowed by anxiety or let yourself be convinced that you'll learn it when you have to. Think deeply about how AI will change the things around you—not society, that's too hard to project—but how it will change your job, your personal life, your immediate environment.

No matter how old you are or young you are, no matter what stage of your career you are in, we are all going through the biggest technological change of the last 100 years, and we're going through it together. Nobody has the answers. It's obvious that so much is going to change, but nobody is going to figure it out before you do if you choose to stay at the frontier.

So don't hide from it. Sit at the front of the class. Pay close attention. And be grateful that it's never been easier to stay at the frontier of the most important technology change of our lifetimes.

[Machina @EXM7777]

https://t.co/XXdYeXQwAo

[Manthan Gupta @manthanguptaa]

https://t.co/CzmfXDgPRP

[Ryo Lu @ryolu_]

on subtraction

adding is easy. someone asks for a feature, you build it. user hits a bug, you patch it. flow feels blocked, you add a shortcut. new trendy idea, you add a new concept. repeat until you have 50 buttons and no one knows where to start.

the hard part isn't building anymore. it's choosing:
- what to make – and what to leave out
- how to make it – so it strengthens the system instead of fragmenting it
- what to remove – even when it works, if it doesn't belong

addition is momentum. subtraction takes conviction.

you have to see the whole, not just the parts. you have to believe that fewer things, done right, will carry more weight than a hundred things scattered.

most products don't die from missing features. they die from accumulation. from losing clarity. from becoming everything and meaning nothing.

think slower, then act fast. slow down after the burst. tie it all back. so the foundation can carry what comes next.

keep focused, and simplify.

[Nicolas Bustamante @nicbstme]

https://t.co/KT2QfXnFad